U.S. patent application number 17/074849 was filed with the patent office on 2021-05-06 for medical imaging system, method for identifying body position of detection object, and storage medium.
The applicant listed for this patent is GE Precision Healthcare LLC. Invention is credited to Qingyu Dai, Yaan Ge, Qilin Lu, Kun Wang, Longqing Wang.
Application Number | 20210128084 17/074849 |
Document ID | / |
Family ID | 1000005304148 |
Filed Date | 2021-05-06 |
United States Patent
Application |
20210128084 |
Kind Code |
A1 |
Dai; Qingyu ; et
al. |
May 6, 2021 |
MEDICAL IMAGING SYSTEM, METHOD FOR IDENTIFYING BODY POSITION OF
DETECTION OBJECT, AND STORAGE MEDIUM
Abstract
Embodiments of the present invention provide a method for
identifying a body position of a detection object in medical
imaging, a medical imaging system, and a computer-readable storage
medium. The method comprises: receiving an image group by a trained
deep learning network, the image group comprising a plurality of
pre-scan images in a plurality of directions obtained by
pre-scanning a detection object; and outputting body position
information of the detection object by the deep learning
network.
Inventors: |
Dai; Qingyu; (Beijing,
CN) ; Lu; Qilin; (Beijing, CN) ; Ge; Yaan;
(Beijing, CN) ; Wang; Kun; (Beijing, CN) ;
Wang; Longqing; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GE Precision Healthcare LLC |
Wauwatosa |
WI |
US |
|
|
Family ID: |
1000005304148 |
Appl. No.: |
17/074849 |
Filed: |
October 20, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 6/488 20130101;
A61B 6/04 20130101; G06N 3/02 20130101; A61B 6/5223 20130101 |
International
Class: |
A61B 6/04 20060101
A61B006/04; A61B 6/00 20060101 A61B006/00; G06N 3/02 20060101
G06N003/02 |
Foreign Application Data
Date |
Code |
Application Number |
Oct 31, 2019 |
CN |
201911050865.8 |
Claims
1. A method for identifying a body position of a detection object
in medical imaging, comprising: receiving an image group by a
trained deep learning network, the image group comprising a
plurality of pre-scan images in a plurality of directions obtained
by pre-scanning a detection object; and outputting body position
information of the detection object by the deep learning
network.
2. The method according to claim 1, wherein the outputting body
position information of the detection object by the deep learning
network comprises: determining one body position type from a
plurality of predetermined body position types to serve as the body
position information of the detection object.
3. The method according to claim 2, wherein the deep learning
network comprises a first learning network and a second learning
network, the method comprising: receiving the image group by the
first learning network, and outputting an image class of each
pre-scan image, the image class comprising a combination of at
least one direction selected from the plurality of directions and
at least one body position type selected from the body position
types; and receiving the image class of each pre-scan image by the
second learning network, and outputting the body position
information of the detection object.
4. The method according to claim 3, wherein the plurality of
directions comprise a coronal direction, a sagittal direction, and
a transverse direction, and each body position type comprises a
combination of an orientation of a head or feet of the detection
object and one position selected from the group consisting of a
supine position, a prone position, a left decubitus position, and a
right decubitus position.
5. The method according to claim 3, wherein the second learning
network extracts an image class of a smaller number from received
image classes, and corrects the extracted image class to be
consistent with an image class of a larger number in the received
image classes.
6. The method according to claim 5, wherein the image class of the
smaller number and the image class of the larger number comprise
the same direction.
7. The method according to claim 1, wherein the deep learning
network comprises a VGG (Visual Geometry Group) convolutional
neural network.
8. A computer-readable storage medium, for storing a computer
program, wherein when executed by a computer, the computer program
causes the computer to perform the method according to claim 1.
9. A medical imaging system, comprising a controller unit, wherein
the controller unit is configured to control a trained deep
learning network to receive an image group, and receive body
position information of a detection object that is output by the
deep learning network, the image group comprising a plurality of
pre-scan images in a plurality of directions obtained by
pre-scanning the detection object by the medical imaging
system.
10. The system according to claim 9, wherein the deep learning
network is configured to determine one body position type selected
from a plurality of predetermined body position types to serve as
the body position information of the detection object.
11. The system according to claim 10, wherein the deep learning
network comprises a first learning network and a second learning
network, the first learning network is configured to receive the
image group and output an image class of each pre-scan image, the
image class comprising a combination of at least one direction
selected from the plurality of directions and at least one body
position type selected from the body position types; and the second
learning network is configured to receive the image class of each
pre-scan image and output the body position information of the
detection object.
12. The system according to claim 11, wherein the plurality of
directions comprise a coronal direction, a sagittal direction, and
a transverse direction, and each body position type comprises a
combination of an orientation of a head or feet of the detection
object and one position selected from the group consisting of a
supine position, a prone position, a left decubitus position, and a
right decubitus position.
13. The system according to claim 11, wherein the second learning
network is configured to extract an image class of a smaller number
from received image classes, and correct the extracted image class
to be consistent with an image class of a larger number in the
received image classes.
14. The system according to claim 13, wherein the image class of
the smaller number and the image class of the larger number
comprise the same direction.
15. The system according to claim 9, wherein the deep learning
network comprises a VGG (Visual Geometry Group) convolutional
neural network.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of Chinese Patent
Application No. 201911050865.8 filed on Oct. 31, 2019, the
disclosure of which is herein incorporated by reference in its
entirety.
TECHNICAL FIELD
[0002] The present invention relates to medical imaging techniques,
and more specifically, to a method for identifying a body position
of a detection object in medical imaging and a computer-readable
storage medium.
BACKGROUND
[0003] In medical imaging techniques such as magnetic resonance
imaging (MRI) and computed tomography (CT) imaging, it is usually
needed to set body position information of a patient during
scanning so as to determine the anatomical section direction (for
example, a coronal direction, a sagittal direction, and a
transverse direction) of an image. Such body position information
includes, for example, the orientation of the head or feet (for
example, expressed as whether the head or the feet enters the
scanning area first when the patient is moved to the scanning area)
and a lying posture (for example, a supine position, a prone
position, a left decubitus position, and a right decubitus
position) of the patient. However, an operator (such as a doctor or
a technician) performing the imaging-scanning often wants to reduce
the need for manual configuration as much as possible to reduce
complexity and time consumption, thereby preventing more serious
problems caused by information input errors. For example, when an
obtained image contains wrong information, it cannot be used in
diagnosis and needs to be manually modified or even re-scanned.
SUMMARY
[0004] An embodiment of the present invention provides a method for
identifying a body position of a detection object in medical
imaging. The method comprises: receiving an image group by a
trained deep learning network, the image group comprising a
plurality of pre-scan images in a to plurality of directions
obtained by pre-scanning a detection object; and outputting body
position information of the detection object by the deep learning
network.
[0005] An embodiment of the present invention further provides a
computer-readable storage medium, for storing a computer program,
wherein when executed by a computer, the computer program causes
the computer to perform the method described above.
[0006] An embodiment of the present invention further provides a
medical imaging system, comprising a controller unit, wherein the
controller unit is configured to control a trained deep learning
network to receive an image group, and receive body position
information of a detection object that is output by the deep
learning network, the image group comprising a plurality of
pre-scan images in a plurality of directions obtained by
pre-scanning the detection object by the medical imaging
system.
[0007] Other features and aspects will become clear through the
following detailed description, accompanying drawings and
claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The present invention may be better understood by describing
exemplary embodiments of the present invention with reference to
accompanying drawings, in which:
[0009] FIG. 1 is a flowchart of a method for identifying a body
position of a detection object in medical imaging according to an
embodiment of the present invention;
[0010] FIG. 2 is an architectural diagram of an example of a VGG
convolutional neural network;
[0011] FIG. 3 is a schematic diagram of using a separate deep
learning network to output body position information;
[0012] FIG. 4 is a schematic diagram of using a first learning
network and a second learning network that are cascaded to output
body position information; and
[0013] FIG. 5 is a structural diagram of an example of a magnetic
resonance imaging-scanning system.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0014] Specific implementation manners of the present invention
will be described in the following. It should be noted that during
the specific description of the implementation manners, it is
impossible to describe all features of the actual implementation
manners in detail in this description for the sake of brief
description. It should be understood that in the actual
implementation of any of the implementation manners, as in the
process of any engineering project or design project, a variety of
specific decisions are often made in order to achieve the
developer's specific objectives and meet system-related or
business-related restrictions, which will vary from one
implementation manner to another. Moreover, it can also be
understood that although the efforts made in such development
process may be complex and lengthy, for those of ordinary skill in
the art related to content disclosed in the present invention, some
changes in design, manufacturing, production or the like based on
the technical content disclosed in the present disclosure are only
conventional technical means, and should not be construed as that
the content of the present disclosure is insufficient.
[0015] Unless otherwise defined, the technical or scientific terms
used in the claims and the description are as they are usually
understood by those of ordinary skill in the art to which the
present invention pertains. The words "first," "second" and similar
words used in the description and claims of the patent application
of the present invention do not denote any order, quantity or
importance, but are merely intended to distinguish between
different constituents. "One," "a(n)" and similar words are not
meant to be limiting, but rather denote the presence of at least
one. The word "include," "comprise" or a similar word is intended
to mean that an element or article that appears before "include" or
"comprise" encompasses an element or article and equivalent
elements that are listed after "include" or "comprise," and does
not exclude other elements or articles. The word "connect,"
"connected" or a similar word is not limited to a physical or
mechanical connection, and is not limited to a direct or indirect
connection.
[0016] Some embodiments of the present invention can automatically
identify body position information of an imaged detection object
based on a deep learning technology, so that the identified body
position information can be further used to determine direction
information of scan images in the imaging process. Thus, the
operator does not need to manually set the body position
information in the imaging process of the detection object. The
operation workload is reduced, and input of wrong body position
information by mistake is avoided. These embodiments can be applied
to magnetic resonance imaging techniques. For example, instead of
providing a body position selection/input window on an operation
interface of a magnetic resonance imaging system, body position
information is automatically identified using images obtained when
a detection object is pre-scanned. These embodiments can further be
applied to other imaging techniques such as CT, PET, and SPECT in a
similar and reasonable manner.
[0017] As discussed herein, the deep learning technology (also
referred to as deep machine learning, hierarchical learning, deep
structured learning, or the like) can employ a deep learning
network (for example, an artificial neural network) to process
input data and identify information of interest. The deep learning
network may be implemented using one or a plurality of processing
layers (such as an input layer, a normalization layer, a
convolutional layer, a pooling layer, and an output layer, where
processing layers of different numbers and functions may exist
according to different deep learning network models), where the
configuration and number of the layers allow the deep learning
network to process complex information extraction and modeling
tasks. Specific parameters (or referred to as "weight" or "bias")
of the network are usually estimated through a so-called learning
process (or training process). The learned or trained parameters
usually result in (or output) a network corresponding to layers of
different levels, so that extraction or simulation of different
aspects of initial data or the output of a previous layer usually
may represent the hierarchical structure or concatenation of
layers. During image processing or reconstruction, this may be
represented as different layers with respect to different feature
levels in the data. Thus, processing may be performed layer by
layer. That is, "simple" features may be extracted from input data
for an earlier or higher-level layer, and then these simple
features are combined into a layer exhibiting features of higher
complexity. In practice, each layer (or more specifically, each
"neuron" in each layer) may process input data as output data for
representation using one or a plurality of linear and/or non-linear
transformations (so-called activation functions). The number of the
plurality of "neurons" may be constant among the plurality of
layers or may vary from layer to layer.
[0018] As discussed herein, as part of the initial training in a
deep learning process to solve a specific problem, a training data
set includes a known input value (for example, a medical image with
RGB depth information or a pixel matrix of the medical image
subjected to coordinate transformation) and an expected (target)
output value (for example, body position information of the
detection object in the image or image class information including
the body position information) that is finally output in the deep
learning process. In this manner, a deep learning algorithm can
process the training data set (in a supervised or guided manner or
an unsupervised or unguided manner) until a mathematical
relationship between a known input and an expected output is
identified and/or a mathematical relationship between the input and
output of each layer is identified and represented. In the learning
process, (part of) input data is usually used, and a network output
is created for the input data. Afterwards, the created network
output is compared with the expected output of the data set, and
then a difference between the created and expected outputs is used
to iteratively update network parameters (weight and/or bias). A
stochastic gradient descent (SGD) method may usually be used to
update network parameters. However, those skilled in the art should
understand that other methods known in the art may also be used to
update network parameters. Similarly, a separate validation data
set may be used to validate a trained learning network, where both
a known input and an expected output are known. The known input is
provided to the trained learning network so that a network output
can be obtained, and then the network output is compared with the
(known) expected output to validate prior training and/or prevent
excessive training.
[0019] FIG. 1 is a flowchart of a method 100 for identifying a body
position of a detection object in medical imaging according to some
embodiments of the present invention. As shown in FIG. 1, the
method 100 includes steps S102 and S104.
[0020] Step S102: receive an image group by a trained deep learning
network, the image group including a plurality of pre-scan images
in a plurality of directions obtained by pre-scanning a detection
object.
[0021] Using magnetic resonance imaging as an example, generally, a
series of images may be reconstructed by performing pre-scanning
(or positioning scanning or basic scanning) on a detection object.
This series of images include, for example, 15 (or other numbers)
pre-scan images, and each pre-scan image has direction information
that can be identified (for example, based on preset body position
information of the detection object). Those skilled in the art
should understand that the direction information is used to
describe the direction of the anatomical plane of the human body,
which may include three basic directions such as a coronal
direction, a sagittal direction, and a transverse direction.
[0022] The plurality of directions in step S102 includes the
coronal direction, the sagittal direction, and the transverse
direction. That is, the image group received by the deep learning
network includes images in three directions such as coronal
pre-scan images, sagittal pre-scan images, and transverse pre-scan
images. For example, the aforementioned 15 pre-scan images may
include 5 coronal image, 5 sagittal images, and 5 transverse
images.
[0023] The trained deep learning network has the ability to
identify the body position of the detection object according to the
group of pre-scan images. Moreover, in step S104, body position
information of the detection object is output by the deep learning
network.
[0024] Step S104 may specifically include: determining one body
position type selected from a plurality of predetermined body
position types to serve as the body position information of the
detection object. For example, the deep learning network may
process the plurality of received pre-scan images to determine the
body position type that the body position of the detection object
most probably belongs to.
[0025] The deep learning network may be implemented through
preparation of training data, selection and construction of a
network model, and training, testing, and optimization of the
network.
[0026] The training data may be medical scan images, such as
magnetic resonance pre-scan images. Specifically, the training data
may include, for example, images obtained when magnetic resonance
pre-scanning is performed at a plurality of known (or determined)
body positions for a plurality of regions of interest (for example,
the abdomen, chest, heart, and head) of the human body. In an
embodiment, the aforementioned deep learning network is obtained by
data training using these images as input of the network model, and
using the plurality of corresponding known body positions as output
of the network model.
[0027] For example, when performing data training, a plurality of
commonly used body position types may be used as output of the deep
learning network, where each body position type includes a
combination of an orientation of a head or feet of the detection
object and one position selected from the group consisting of a
supine position, a prone position, a left decubitus position, and a
right decubitus position. For example, these body position types
may include, for example, eight body positions shown in FIG. 2,
where HFS represents Head First, Supine; HFP represents Head First,
Prone; HFDR represents Head First, Right Decubitus; HFDL represents
Head First, Left Decubitus; FFS represents Feet First, Supine; FFP
represents Feet first, Prone; FFDR represents Feet First, Right
Decubitus; FFDL represents Feet First, Left Decubitus. "Feet First"
represents that the feet of the detection object are close to the
scanning area (for example, the scanning chamber) when the
detection object is on the detection table, and the feet of the
detection object enter the scanning area first when the detection
table is moved to make the detection object thereon enter the
scanning chamber from the outside. The same logic can be applied to
the definition of "Head First."
[0028] In some embodiments, the deep learning network is trained
based on a VGGNet (Visual Geometry Group Network) convolutional
neural network or other well-known models. For example, a VGG-11
network may be selected to implement the embodiment of the present
invention. FIG. 2 is a schematic architectural diagram of the
VGG-11 network, which specifically includes an input layer, an
output layer, 5 groups of convolutional layers (a total of 8
convolutional layers) located between the input layer and the
output layer, and 3 fully-connected layers, where each group of
convolutional layers is further connected to a pooling layer. The
size of each convolution kernel is 3.times.3 (pixels), the size of
each pooling kernel is 2.times.2 (pixels), and the number of
convolution kernels in each group of convolutional layers is
separately 64, 128, 256, 512, and 512. In training, a large number
of image groups are input to the deep learning network. Each image
group includes a plurality of images having the same known body
position type (for example, each image group is an image group
obtained by one pre-scan or scan), the size of each image may be,
for example, 224.times.224.times.3. The known body position type is
accordingly set as output of the deep learning network (or as will
be described later--a large number of images having a known image
class are input to the deep learning network, and the known image
class is accordingly used as output of the deep learning network).
The weight at each pixel in each layer and other required
parameters are determined by convolutional learning to identify a
mathematical relationship between the known input and output and/or
represent a mathematical relationship between the input and output
of each layer, so as to establish a deep learning network capable
of identifying a body position (or an image class). Each
convolutional layer is used to perform convolution processing on
image data output by an upper level, so as to obtain a feature map
of the convolutional layer. The pooling layer is used to perform,
for example, max-pooling on the feature map of the upper-layer
convolutional layer to extract main features. The fully-connected
layer is used to integrate the upper-layer feature map, and the
output layer is used to output a judgment result (i.e., body
position information or the image class described below) obtained
by, for example, logistic regression processing.
[0029] In the learning process, comparison may be performed based
on the network output (body position information or image class)
corresponding to the known input (pre-scan image group) and actual
information (known body position information or image class), and a
difference thereof is a loss function. The loss function may be
used to iteratively update parameters (weight and/or to bias) of
the network so that the loss function continuously decreases to
train a neural network model with higher accuracy. In some
embodiments, many functions can be used as the loss function,
including, but not limited to, mean squared error (mean squared),
cross entropy error, and the like.
[0030] FIG. 3 is a schematic diagram of using a separate deep
learning network to output body position information. As shown in
FIG. 3, in an embodiment, during a process such as magnetic
resonance scanning, a series of obtained pre-scan images may be
input to an input layer of the deep learning network, and then
these images are processed based on parameters such as the weight
determined in the training process, so as to output, on the output
layer, a body position type of a detection object currently being
scanned.
[0031] FIG. 4 is a schematic diagram of using a first learning
network and a second learning network that are cascaded to output
body position information. As shown in FIG. 4, the aforementioned
deep learning network may include a first learning network 402 and
a second learning network 404, the first learning network 402 may
have the aforementioned VGG-11 architecture or a similar
architecture, and the second learning network 404 may be a
classifier having a simple architecture.
[0032] The first learning network 402 is configured to receive the
aforementioned image group and output an image class of each
pre-scan image. The image class includes a combination of at least
one direction selected from the aforementioned plurality of
directions (the coronal direction, sagittal direction, and
transverse direction) and at least one body position type selected
from the plurality of body position types (HFS, HFP, HFDR, HFDL,
FFS, FFP, FFDR, and FFDL). That is, the first learning network 402
can be used to judge possible directions of the pre-scan images and
possible body positions of the detection object therein.
[0033] In training, the input layer of the first learning network
402 receives a plurality of images with a size of, for example,
256.times.256.times.3, and a known image class corresponding to
each image is set on the output layer, and the weight at each pixel
in each layer and other required parameters are determined by
convolutional learning, so as to establish a deep learning network
capable of identifying an image class.
[0034] Thus, in magnetic resonance scanning, a series of obtained
pre-scan images may be input to the input layer of the first deep
learning network, and then these images are processed based on the
parameters such as the weight determined in the training process,
so as to output, on the output layer, an image class of each
pre-scan image.
[0035] The aforementioned image class may be one image class
selected from a plurality of known image classes in the following
table:
[0036] Class A: A combination of the transverse direction, HFS, and
FFS, which indicates that the pre-scan image is a transverse image,
where the body position of the detection object is HFS or FFS;
[0037] Class B: A combination of the transverse direction, HFP, and
FFP, which indicates that the pre-scan image is a transverse image,
where the body position of the detection object is HFP or FFP;
[0038] Class C: A combination of the transverse direction, HFDR,
and FFDL, which indicates that the pre-scan image is a transverse
image, where the body position of the detection object is HFDR or
FFDL;
[0039] Class D: A combination of the transverse direction, HFDL,
and FFDR, which indicates that the pre-scan image is a transverse
image, where the body position of the detection object is HFDL or
FFDR;
[0040] Class E: A combination of the sagittal direction, the
coronal direction, HFS, HFP, HFDR, and HFDL, which indicates that
the pre-scan image is a sagittal image or coronal image, where the
body position of the detection object is HFS, HFP, HFDR, or
HFDL;
[0041] Class F: A combination of the sagittal direction, the
coronal direction, FFS, FFP, FFDR, and FFDL, which indicates that
the pre-scan image is a sagittal image or coronal image, where the
body position of the detection object is FFS, FFP, FFDR, or
FFDL.
[0042] The foregoing classification manner of image classes helps
to obtain accurate body position information. Certainly, the
embodiment of the present invention may further employ image
classes of other numbers and classification manners to train the
aforementioned first learning network and second learning network,
so as to obtain more accurate body position information or achieve
other beneficial effects.
[0043] In training, the second learning network 404 may receive
known image types of image groups generated in a plurality of
pre-scanning processes, and set, on the output layer, a body
position of the detection object for performing each pre-scan. The
weight at each pixel in each layer and other required parameters
are determined by convolutional learning, so as to establish a
second learning network capable of identifying a body position
based on an image class.
[0044] Thus, after the first learning network 402 outputs the image
class of each pre-scan image to the second learning network, the
second learning network 404 may output the body position
information of the detection object.
[0045] First, the first learning network 402 analyzes an input
image group to obtain an image class of each pre-scan image, where
at least the possible direction and body position type of each
pre-scan image can be obtained according to the image class, and
then the second learning network 404 analyzes the image class of
each input pre-scan image, so that correct body position
information of the detection object can be obtained. In this way,
the accuracy of body position identification can be greatly
improved. For example, at least the problem of accuracy reduction
due to the excessively large computation amount can be avoided.
[0046] For example, if the first learning network 402 receives 10
pre-scan images and outputs image classes of the first to the tenth
pre-scan images including 4 image classes being Class D and 6 image
classes being Class E, the second learning network 404 can output
the accurate body position of the detection object, namely,
HFDL.
[0047] Further, the second learning network 404 is configured to
extract an image class of a smaller number from received image
classes, and correct the extracted image class to be consistent
with an image class of a larger number in the received image
classes. This may be accomplished by setting a preprocessing layer
in the second learning network 404. First, an image class of each
pre-scan image is output, so that there is a chance to correct
wrong image classes in the second learning network 404, so that the
finally output body position information has high accuracy.
[0048] For example, if the first learning network 402 receives 10
pre-scan images and outputs image classes of the first to the tenth
pre-scan images including 3 image classes being Class D, 6 image
classes being Class E, and 1 image class being Class C, the second
learning network 404 corrects Class C to Class D or Class E through
preprocessing, and then processes preprocessed data to output the
accurate body position of the detection object, namely, HFDL.
[0049] Further, the second learning network 404 judges the
direction in the image class of the smaller number and corrects the
image class to an image class of a larger number including the
direction. That is, the modified image class of the smaller number
and the image class of the larger number have the same direction.
For example, since the direction in Class C is the transverse
direction, Class C is corrected to Class D which is pointing to the
transverse direction rather than Class E which is pointing to the
sagittal direction.
[0050] In the medical imaging-scanning process, when the
pre-scanning ends, formal scanning may be performed on a detection
object to obtain a plurality of medical diagnostic images of the
detection object. Thus, after body position information of the
detection object is identified based on any embodiment of the
method 300 and medical diagnostic images are obtained based on
formal scanning, direction information of each medical diagnostic
image may be determined based on the identified body position
information.
[0051] The body position of the detection object is automatically
identified, so that even if a doctor or a technician is no longer
required to manually set body position information, direction
information of each reconstructed image can be determined as in the
conventional scanning process. The scanning process is smoother,
and the scanning result also has higher robustness. For example, a
medical imaging system may judge the direction of a corresponding
pre-scan image based on automatically identified body position
information, HFS (Head First, Supine), and an employed scanning
sequence.
[0052] The method described above can be used in magnetic resonance
or other medical imaging-scanning techniques. FIG. 5 is a
structural diagram of an example of a magnetic resonance
imaging-scanning system. An example of applying the aforementioned
medical imaging-scanning method to a magnetic resonance
imaging-scanning technique will be described below with reference
to FIG. 5.
[0053] As shown in FIG. 5, the magnetic resonance imaging system
500 includes a scanner 510, a table 520, a controller unit 530, a
data processing unit 540, an operating console 550, and a display
unit 560.
[0054] In an example, the scanner 510 may include a main magnet
assembly 511. The main magnet assembly 511 usually includes an
annular superconducting magnet defined in a housing, where the
annular superconducting magnet is mounted in an annular vacuum
container. The annular superconducting magnet and the housing
thereof define a cylindrical space, i.e., the scanning chamber 512
shown in FIG. 5, which surrounds the detection object 56. The
scanning chamber 512 defines an imaging area of the magnetic
resonance imaging system or at least part of the imaging area.
[0055] The table 520 is configured to be communicable with a
patient entrance of the scanning chamber 512 and is configured to
carry the detection object 56, so that the detection object 56 in a
specific body position (for example, one body position type
selected from the aforementioned plurality of body position types)
can be moved to the scanning area to receive imaging-scanning.
[0056] The scanner 510 further includes an RF transmit coil 516, a
radio-frequency generator 513, a gradient coil assembly 514, a
gradient coil driver 515, an RF receive coil 570, and a data
acquisition unit 517. When an imaging-scanning process is performed
on the detection object 56, the scanner 510 is configured to obtain
image data of the detection object 56.
[0057] The image data may be processed, such as calculated or
reconstructed, by the data processing unit 540. The data processing
unit 540 may include a computer and a storage medium, where a
program of predetermined data processing to be executed by the
computer is recorded on the storage medium.
[0058] The controller unit 530 is coupled to the scanner 510, the
table 520, and the data processing unit 540 to control these
components to perform a scanning process for magnetic resonance
imaging. The scanning process may specifically include a
pre-scanning process and a formal scanning process.
[0059] The controller unit 530 may include a computer and a storage
medium, where the storage medium is configured to store a program
executable by the computer, and when the computer executes the
program, the components such as the scanner 510, the table 520, and
the display unit 560 are enabled to perform corresponding
operations in the pre-scanning process and the scanning process.
The data processing unit 540 is also enabled to perform
predetermined data processing.
[0060] The storage media of the controller unit 530 and the data
processing unit 540 may include, for example, a ROM, a floppy disk,
a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, or
a non-volatile memory card.
[0061] The operating console 550 may include a user input device,
such as a keyboard and a mouse, where an operator may input control
signals or parameter settings to the controller unit 530 through
the user input device, and these parameter settings usually include
setting of body position information of the detection object.
However, this step may be omitted in this embodiment; instead, the
body position information of the detection object is automatically
identified by the deep learning network.
[0062] The display unit 560 may be connected to the operating
console 550 to display an operation interface that includes a
parameter setting interface, and may further be connected to the
data processing unit 540 to display reconstructed images or various
images captured by a camera that is coupled to the magnetic
resonance imaging system.
[0063] In performing magnetic resonance imaging-scanning, a doctor
or a technician usually needs to assist or instruct the detection
object to be positioned on the table 520 according to a desired
body position. Then, body position information of the detection
object is set on the operation interface before pre-scanning is
performed, and if the set body position information is inconsistent
with the actual positioning posture of the detection object, it
takes time to fix such errors, or requires a re-scanning.
[0064] By using the method of the present invention, the body
position is not set; instead, the body position is determined as
follows: first, pre-scanning is performed by the scanner to obtain
a group of pre-scan images; then, the pre-scan images are used to
identify a body position of a detection object; after a region of
interest is determined based on the pre-scan images, formal
scanning may be performed on the region of interest; the data
processing unit 540 reconstructs a medical diagnostic image based
on data obtained in formal scanning; and direction information of
the medical diagnostic image may be marked while the medical
diagnostic image is displayed by the display unit. As a result, the
doctor would not be confused with the direction of the image even
if the body position is not set.
[0065] In an embodiment, the aforementioned trained deep learning
network is obtained based on training by a training system on an
external carrier (for example, a device other than the medical
imaging system). In some embodiments, the training system may
include a first module configured to store a training data set, a
second module configured to perform training and/or update based on
a model, and a network configured to connect the first module and
the second module. In some embodiments, the first module includes a
first processing unit and a first storage unit, where the first
storage unit is configured to store the training data set, and the
first processing unit is configured to receive a relevant
instruction (for example, obtaining a training data set) and send
the training data set to the second module according to the
instruction. The second module includes a second processing unit
and a second storage unit, where the second storage unit is
configured to store a training model, and the second processing
unit is configured to receive a relevant instruction and perform
training and/or update of the learning network. In some
embodiments, the network may include various connection types, such
as wired or wireless communication links, or fiber-optic
cables.
[0066] Once a deep learning network is generated and/or configured,
the data thereof can be replicated and/or loaded into the
aforementioned magnetic resonance imaging system 500, which may be
accomplished in a different manner. For example, models may be
loaded via a directional connection or link between the controller
unit 530 of the magnetic resonance imaging system 500 and the
second storage unit. In this regard, communication between
different elements may be accomplished using an available wired
and/or wireless connection and/or based on any suitable
communication (and/or network) standard or protocol. Optionally,
the data may be indirectly loaded into the magnetic resonance
imaging system 500. For example, the data may be stored in a
suitable machine-readable medium (for example, a flash memory
card), and then the medium is used to load the data into the
magnetic resonance imaging system 500 (for example, by a user or an
authorized person of the system on site). Alternatively, the data
may be downloaded to an electronic device (for example, a notebook
computer) capable of local communication, and then the device is
used on site (for example, by a user or an authorized person of the
system) to upload the data to the magnetic resonance imaging system
500 via a direct connection (for example, a USB connector).
[0067] Based on the above description, an embodiment of the present
invention may further provide a medical imaging system, including a
controller unit, where the controller unit is configured to control
a trained deep learning network to receive an image group, and
receive body position information of a detection object that is
output by the deep learning network, the image group including a
plurality of pre-scan images in a plurality of directions obtained
by pre-scanning the detection object by the medical imaging
system.
[0068] The deep learning network is specifically configured to
determine one body position selected from a plurality of
predetermined body position types to serve as the body position
information of the detection object.
[0069] The deep learning network may include a first learning
network 402 and a second learning network 404. The first learning
network 402 is configured to receive the image group, and output an
image class of each pre-scan image, the image class including a
combination of at least one direction selected from the plurality
of directions and at least one body position type selected from the
plurality of body position types. The second learning network 404
is configured to receive the image class of each pre-scan image,
and output the body position information of the detection
object.
[0070] The second learning network 404 is further configured to
extract an image class of a smaller number from received image
classes, and correct the extracted image class to be consistent
with an image class of a larger number in the received image
classes. The image class of the smaller number and the image class
of the larger number include the same direction.
[0071] An embodiment of the present invention may further provide a
computer-readable storage medium for storing an instruction set
and/or a computer program. When executed by a computer, the
instruction set and/or computer program causes the computer to
perform the method for identifying a body position of a detection
object according to any embodiment described above. The computer
executing the instruction set and/or computer program may be a
computer of a medical imaging system, or may be other
apparatuses/modules of the medical imaging system. In an
embodiment, the instruction set and/or computer program may be
programmed into a processor/controller, for example, the
aforementioned controller unit 530, of the computer.
[0072] Specifically, when executed by the computer, the instruction
set and/or computer program causes the computer to:
[0073] identify an image quality type of a medical image based on a
trained learning network; and
[0074] generate, based on the identified image quality type, a
corresponding control signal for controlling the medical imaging
system.
[0075] The instructions described above may be combined into one
instruction for execution, and any of the instructions may also be
split into a plurality of instructions for execution. Moreover, the
present invention is not limited to the instruction execution order
described above.
[0076] In some embodiments, before identifying an image quality
type in a medical image, the method further includes receiving,
based on an instruction of a user, the medical image generated by
the medical imaging system.
[0077] As used herein, the term "computer" may include any
processor-based or microprocessor-based system including a system
that uses a microcontroller, a reduced instruction set computer
(RISC), an application specific integrated circuit (ASIC), a logic
circuit, and any other circuit or processor capable of executing
the functions described herein. The above examples are merely
exemplary and thus are not intended to limit the definition and/or
meaning of the term "computer" in any way.
[0078] The instruction set may include various commands that
instruct a computer acting as a processor or instruct a processor
to perform particular operations, such as the methods and processes
of various embodiments. The instruction set may be in the form of a
software program, and the software program can form part of one or
a plurality of tangible, non-transitory computer-readable media.
The software may be in various forms such as system software or
application software. In addition, the software may be in the form
of a set of independent programs or modules, a program module
within a larger program, or part of a program module. The software
may also include modular programming in the form of object-oriented
programming. The input data may be processed by the processor in
response to an operator command, or in response to a previous
processing result, or in response to a request made by another
processor.
[0079] Some exemplary embodiments have been described above;
however, it should be understood that various modifications may be
made. For example, if the described techniques are performed in a
different order and/or if the components of the described system,
architecture, device, or circuit are combined in other manners
and/or replaced or supplemented with additional components or
equivalents thereof, a suitable result can be achieved.
Accordingly, other implementation manners also fall within the
protection scope of the claims.
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